Non-linear media based computers: chemical and neuronal networks through machine learning

Bull, L., Adamatzky, A., de Lacy Costello, B., Husbands, P., O’Shea, M., Purcell, W. and Taylor, A. (2008) Non-linear media based computers: chemical and neuronal networks through machine learning. Project Report. UNSPECIFIED, UK. Available from: http://eprints.uwe.ac.uk/20700

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Abstract/Description

There is growing interest in research into the development of hybrid wetware-silicon devices focused on exploiting their potential for 'non-linear computing'. The aim is to harness the as yet only partially understood intricate dynamics of non-linear media to perform complex 'computations' (potentially) more effectively than with traditional architectures and to further the understanding of how such systems function. The area provides the prospect of radically new forms of machines and is enabled by improving capabilities in wetware-silicon interfacing. The research proposed here will present an approach by which networks of non-linear media - neurons and reaction-diffusion systems - can be produced to achieve a user-defined computation (or behaviour) in a way that allows control of the media used and the substrate in which they exist. Simulated evolutionary algorithms will be used to design the appropriate network structures by searching a defined space of preparation procedures to create a computing resource capable of satisfying a given objective(s).

Item Type:Report or Working Paper (Project Report)
Uncontrolled Keywords:biomedical neuroscience, gas and solution phase reactions, fundamentals of computing, new and emerging computer paradigms
Faculty/Department:Faculty of Environment and Technology > Department of Computer Science and Creative Technologies
ID Code:20700
Deposited By: M. Clarke
Deposited On:17 Jul 2013 11:01
Last Modified:12 Apr 2016 13:06

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